Abstract
This brief proposes a novel control scheme for tracking a differential drive wheeled mobile robot. A neural network-based tilt integral derivative controller is introduced to track the wheeled mobile robot. Conventional control algorithms fail to satisfy the desired performance in terms of accuracy, total control effort, and maximum control input. By properly training a neural network, optimal values of the parameters of the tilt integral derivative (TID) controller are obtained. The proposed controller is used to track circular, lemniscate, straight line, and B spline trajectories. Performance of the proposed controller is compared with conventional TID and PID controllers using different performance analysis indices. Simulation studies show that the integral squared error and total control effort are small for the proposed controller as compared to the TID and PID controllers.
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Hassan, N., Saleem, A. (2022). Neural Network-Based TID Controller for Wheeled Mobile Robot Trajectory Tracking. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 235. Springer, Singapore. https://doi.org/10.1007/978-981-16-2377-6_21
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DOI: https://doi.org/10.1007/978-981-16-2377-6_21
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